Processing for multi-channel signals

ABSTRACT

Method and apparatus for improved processing for multi-channel signals. In an exemplary embodiment, an anomaly metric is computed for a multi-channel signal over a time window. The magnitude of the anomaly metric may be used to determine whether an anomaly is present in the multi-channel signal over the time window. In an exemplary embodiment, the anomaly metric may be a condition number associated with the singular values of the multi-channel signal over the time window, as further adjusted by the number of channels to produce a data condition number. Applications of the anomaly metric computation include the scrubbing of signal archives for epileptic seizure detection/prediction/counter-prediction algorithm training, pre-processing of multi-channel signals for real-time monitoring of bio-systems, and boot-up and/or adaptive self-checking of such systems during normal operation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/183,449, filed Jun. 2, 2009, which is incorporated herein byreference in its entirety.

TECHNICAL FIELD

The present disclosure relates generally to methods for processingmulti-channel signals. In particular, the present disclosure relates toimproved processing of multi-channel signals by detecting and/ortreating possible anomalies in the multi-channel signals

INCORPORATION BY REFERENCE

All publications and patent applications mentioned in this specificationare herein incorporated by reference to the same extent as if eachindividual publication or patent application was specifically andindividually indicated to be incorporated by reference.

BACKGROUND OF THE INVENTION

A common task encountered in the field of signal processing is thesampling and processing of a physical state using multiple, ideallyindependent, signal sensors. The diversity of the resulting multi-sensoror multi-channel signal typically reveals more information about theunderlying sampled state than can be obtained from employing a singlesensor.

Multi-channel signal processing is utilized in biomedical applications.For example, in the field of neurological monitoring for epilepticseizure prediction, multiple electrodes may be implanted in diverselocations on or in a patient's brain to monitor the susceptibility ofthe patient to enter into an epileptic seizure. The multi-channelsignals generated by the electrodes may be processed to, e.g., alert thepatient and/or medical personnel of a high likelihood of imminentseizure. See, e.g., U.S. Patent Publication No. 2008/0183096 A1,“Systems and Methods for Identifying a Contra-ictal Condition in aSubject,” filed Jan. 25, 2008, assigned to the assignee of the presentapplication, the contents of which are hereby incorporated by referencein their entirety. The signals may also be stored and processed offlineto, e.g., train customized algorithms for estimating the likelihood thata patient will experience an imminent seizure. See, e.g., U.S. Pat. No.6,678,548, “Unified probabilistic framework for predicting and detectingseizure onsets in the brain and multitherapeutic device,” the contentsof which are hereby incorporated by reference in their entirety.

Other applications of multi-channel signal processing include thereception of wireless signals by a communications device using multipleantennas, geological monitoring of seismic activity for earthquakeprediction, stereo imaging using multiple video cameras, etc.

When multi-channel signals are sampled over an extended period of time,artifacts or anomalies often appear in the signal. Such anomalies may bedue to interference from external sources, disruptions to the powersupply of the sensors, and/or other sources. Left untreated, suchanomalies may degrade the quality of the measured signal and disrupt theaccuracy of any subsequent processing of the multi-channel signal.

It would be desirable to have techniques to detect the presence ofanomalies in a multi-channel signal, and to optimize the processing of asignal containing such anomalies.

SUMMARY OF THE INVENTION

An aspect of the present disclosure provides a method for displayinginformation associated with a multi-channel signal to a user, the methodcomprising: accepting input from the user selecting a metric to bedisplayed; displaying at least one time-series plot of the selectedmetric associated with the multi-channel signal; using a backdroppattern, indicating on the at least one time-series plot portions of theplot having at least one identified characteristic; for each of the atleast one time-series plot displayed, displaying a time event indexwherein the corresponding metric meets a predetermined condition;accepting input from the user as to whether to display furtherinformation associated with the time event index; and displaying thefurther information associated with the time event index if the user sospecifies.

Another aspect of the present disclosure provides a method for detectinganomalies in a multi-channel signal, the method comprising: sampling themulti-channel signal over a time window; computing an anomaly metric forthe multi-channel signal over the time window; and identifying thepresence of an anomaly based on the magnitude of the anomaly metric; thecomputing an anomaly metric comprising: computing a condition number ofthe multi-channel signal over the time window; and adjusting thecondition number based on a parameter of the multi-channel signal togenerate a data condition number (DCN); the identifying the presence ofan anomaly comprising comparing the magnitude of the data conditionnumber (DCN) to at least one threshold; the method further comprisinggenerating one of the at least one threshold, the generating comprising:generating a histogram of number of anomalies detected for each of aplurality of candidate thresholds; generating a cumulative distributionfunction from the histogram, the cumulative distribution functionmapping each candidate threshold to a percentage value; and determiningthe generated threshold as the candidate threshold mapped to acorresponding predetermined percentage value by the cumulativedistribution function.

Yet another aspect of the present disclosure provides a method fordetecting anomalies in a multi-channel signal, the method comprising:sampling the multi-channel signal over a time window; computing ananomaly metric for the multi-channel signal over the time window; andidentifying the presence of an anomaly based on the magnitude of theanomaly metric; the computing an anomaly metric comprising: computing acondition number of the multi-channel signal over the time window; andadjusting the condition number based on a parameter of the multi-channelsignal to generate a data condition number (DCN); the identifying thepresence of an anomaly comprising comparing the magnitude of the rate ofchange of the data condition number (DCN) to at least one threshold.

Yet another aspect of the present disclosure provides a method fordetecting anomalies in a multi-channel signal, the method comprising:sampling the multi-channel signal over a time window; computing ananomaly metric for the multi-channel signal over the time window; andidentifying the presence of an anomaly based on the magnitude of theanomaly metric; the computing an anomaly metric comprising: computing acondition number of the multi-channel signal over the time window; andadjusting the condition number based on a parameter of the multi-channelsignal to generate a data condition number (DCN); the multi-channelsignal comprising a signal sampled from a plurality of electrodesimplanted on or in a patient's brain, the method further comprising:generating a DCN time series corresponding to a plurality of timewindows; generating an anomaly log based on the DCN time series; merginganomalies in the anomaly log separated by less than a minimum separationto generate a modified anomaly log; identifying segments of themulti-channel signal corresponding to anomalies in the modified anomalylog; and outputting time-expanded versions of the identified segments toa record; the identifying the presence of an anomaly comprising matchingthe DCN time series to at least one known pattern of DCN time seriescorresponding to an anomalous condition.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an exemplary embodiment of the present disclosure of asystem for quality management of measured electrical signals generatedby electrodes placed on or within the brain.

FIGS. 2A, 2B and 2C illustrate possible signal anomalies that may bedetected by the anomaly detector/processor in the multi-channel signal.

FIG. 3A depicts an exemplary method according to the present disclosure,wherein a metric known as a “data condition number,” or DCN, is derivedfor a given multi-channel signal.

FIG. 3B shows a matrix A[k] derived from the multi-channel signal.

FIG. 4A illustrates a data condition number (DCN) time series obtainedfrom a sample multi-channel signal using the steps of FIG. 3A andthresholding for event detection.

FIG. 4B illustrates a plot used in a procedure by which specificthresholds may be automatically chosen for any patient or group ofpatients.

FIGS. 4C and 4D illustrate an alternative exemplary embodiment of atechnique for choosing an optimum threshold T1*.

FIG. 5 depicts an exemplary method according to the present disclosurefor taking action in response to the detection of an anomaly.

FIG. 6 depicts an alternative exemplary embodiment of the presentdisclosure, wherein the techniques disclosed hereinabove are applied inthe context of a real-time patient monitoring and neurological eventdetection system.

FIG. 7 depicts a generalized block diagram of the real-time analysissystem featuring an anomaly pre-processing block according to thepresent disclosure.

FIG. 8 depicts an exemplary method according to the present disclosurethat may be implemented, e.g., using the system shown in FIG. 7.

FIGS. 9A and 9B illustrate exemplary plots of data condition number(DCN) versus time, corresponding to certain specific operatingscenarios.

FIGS. 10A and 10B illustrate an exemplary embodiment of a graphicaldisplay interface for conveniently displaying information associatedwith the techniques of the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

The detailed description set forth below in connection with the appendeddrawings is intended as a description of exemplary embodiments of thepresent invention and is not intended to represent the only exemplaryembodiments in which the present invention can be practiced. The term“exemplary” used throughout this description means “serving as anexample, instance, or illustration,” and should not necessarily beconstrued as preferred or advantageous over other exemplary embodiments.The detailed description includes specific details for the purpose ofproviding a thorough understanding of the exemplary embodiments of theinvention. It will be apparent to those skilled in the art that theexemplary embodiments of the invention may be practiced without thesespecific details. In some instances, well known structures and devicesare shown in block diagram form in order to avoid obscuring the noveltyof the exemplary embodiments presented herein.

While the discussion below focuses on measuring electrical signalsgenerated by electrodes placed near, on, or within the brain or nervoussystem (EEG signals) of subjects and subject populations for thedetermination of when an epileptic subject is in a condition susceptibleto seizure, it should be appreciated that the techniques of the presentdisclosure are not limited to measuring EEG signals or to determiningwhen the subject is susceptible to seizure. For example, the techniquescould also be used in systems that measure one or more of a bloodpressure, blood oxygenation (e.g., via pulse oximetry), temperature ofthe brain or of portions of the subject, blood flow measurements,ECG/EKG, heart rate signals, respiratory signals, chemicalconcentrations of neurotransmitters, chemical concentrations ofmedications, pH in the blood, or other physiological or biochemicalparameters of a subject.

The present disclosure may also be applicable to monitoring otherneurological or psychiatric disorders and identifying a condition orstate for such disorders in which the subject is unlikely to experiencesome adverse effect. For example, the present disclosure may also beapplicable to monitoring and management of sleep apnea, Parkinson'sdisease, essential tremor, Alzheimer's disease, migraine headaches,depression, eating disorders, cardiac arrhythmias, bipolar spectrumdisorders, or the like.

Non-biomedical applications of the techniques described herein are alsocontemplated to be within the scope of the present disclosure.

FIG. 1 depicts an exemplary embodiment of the present disclosure of asystem for quality management of measured electrical signals generatedby electrodes implanted into the brain, subdurally, epidurally,partially or fully in the skull, between the skull and one or morelayers of the patient's scalp, or on the exterior of the patient's head.In some embodiments, the electrodes are configured to be insertedthrough a single opening in a skull of a patient and to be dispersed ona surface of the brain. The surface is preferably the cortical surfaceof the brain, but may alternatively be any other suitable surface orlayer of the brain, such as the dura mater. Note the exemplaryembodiment depicted in FIG. 1 is shown for illustrative purposes only,and is not meant to limit the scope of the present disclosure to anyparticular embodiment shown.

In FIG. 1, electrodes 110 are implanted on or in the brain of a patient100, e.g., underneath the dura mater and on a cortical surface of thepatient's brain. Each of the electrodes generates a corresponding signalthat is input to a data pre-processing module 120. The electrodes may bedirectly connected to the data pre-processing module 120 through aseries of electrical leads 115 a, or they may be wirelessly connected tothe data pre-processing module 120 over a wireless link. In an exemplaryembodiment, data pre-processing module 120 may, e.g., digitize theplurality of received electrical signals 115 b to generate amulti-channel signal 120 a for further processing.

Multi-channel signal 120 a is input to an anomaly detector/processor130. In an exemplary embodiment, the anomaly detector/processor 130 mayutilize techniques further disclosed hereinbelow to identify thepresence of signal anomalies in the multi-channel signal 120 a. Asfurther disclosed hereinbelow, anomaly detector/processor 130 may alsotake further action to address the anomalies detected, e.g., flaggingthe portions of the multi-channel signal corresponding to the detectedanomalies in a log file 130 a for optional manual review by a humanoperator.

In the exemplary embodiment shown, the log file 130 a from the anomalydetection/processing module 130 is provided along with the multi-channelsignal 120 a to a data processing/adaptive algorithm training module140. In an exemplary embodiment, module 140 may utilize themulti-channel signal 120 a, coupled with information from the log file130 a about which portions of the multi-channel signal 120 a containanomalies, to train an adaptive algorithm to identify conditions underwhich patient 100 is susceptible to seizure. An exemplary system isdescribed in Snyder, et al., “The statistics of a practical seizurewarning system,” Journal of Neural Engineering, vol. 5, pp. 392-401(2008), the contents of which are incorporated by referenced herein inits entirety. In an exemplary embodiment, module 140 may, e.g.,automatically de-emphasize portions of multi-channel signal 120 acorresponding to signal anomalies, and emphasize other portions of thesignal 120 a, to configure adaptive weights for a seizure predictionalgorithm 140 a. In alternative exemplary embodiments, a human operatormay manually review portions of multi-channel signal 120 a that havebeen flagged in the log file 130 a, and decide whether such portions maybe used for adaptive algorithm training.

In an exemplary embodiment, the log file 130 a need not be limited to asingle file residing in a single piece of storage hardware. For example,the log file 130 a can be an extensive archive of intracranial EEGpatterns that can be used to develop a predictive neurosensing devicefor managing seizures by mining the archive for signal patterns over thepatient population. This archive may be stored for multi-user access andprocessing in, e.g., a server or cloud computing system.

FIGS. 2A, 2B, and 2C illustrate possible signal anomalies that may bedetected by the anomaly detector/processor 130 in the multi-channelsignal 120 a. Note the anomaly patterns identified in FIGS. 2A, 2B, and2C are for illustrative purposes only, and are not meant to limit thescope of the present disclosure to any particular anomaly patternshighlighted.

In FIG. 2A, reference numeral 210 points to an instance of a “spikeswithout phase reversal” anomaly in a referential multi-channel signal,or “spikes without double-phase reversal” anomaly in a bipolarmulti-channel signal, possibly due to non-physiological interferencesources. Reference numeral 220 points to an instance of a “flatline”anomaly in the multi-channel signal. In FIG. 2B, reference numeral 230points to an instance of a “line noise” anomaly in the multi-channelsignal. Such an anomaly may correspond to, e.g., powerline noise, i.e.,50 or 60 Hz noise and/or harmonics superimposed on the multi-channelsignal. In FIG. 2C, reference numeral 240 points to an instance of a“saturation” anomaly in the multi-channel signal.

Other possible anomalies in a multi-channel signal (not shown) includeepisodic artifacts such as motion (large swings in the multi-channelsignal), DC shifts (different DC levels between different channels oracross a single channel), pops (exponential decay from amplifierhighpass characteristic of a sudden change in the DC level of a signal),and glitches (e.g., 50 ms burst transients in the signal). Long-termanomalies may include deterioration trends in the system, and/orchannels of persistently poor quality. Such anomalies and others notexplicitly enumerated are contemplated to be within the scope of thepresent disclosure.

FIG. 3A depicts an exemplary method 300 according to the presentdisclosure, wherein a specific anomaly metric known as a “data conditionnumber,” or DCN, is derived for a given multi-channel signal. In anexemplary embodiment, the DCN may be used to help identify the presenceof anomalies in the multi-channel signal. The DCN as described withreference to FIG. 3A has been found to provide a reliable indicator forthe presence of anomalies in a multi-channel signal, and is readilyapplicable to a wide variety of scenarios.

In FIG. 3A, at step 310, the number of dimensions of a multi-channelsignal is defined as the variable N. The number of dimensions maycorrespond to the total number of sensors, e.g., the number ofindependent electrodes placed in a patient's brain in the epilepsymonitoring system of FIG. 1.

At step 320, the multi-channel signal is divided in time into windows ofduration T, with each of the time windows being indexed by a counter k.According to the present disclosure, the multi-channel signal may be adiscrete-time signal sampled at a rate of S Hz. In this case, each timewindow k may contain a total of T·S discrete-time samples multiplied byN channels (or sensors), which may be arranged to form a T·S matrix by NA[k] at step 330. The matrix A[k] is also shown in FIG. 3B. In anexemplary embodiment, the matrix A[k] is generally rectangular.

In an exemplary embodiment, T may be 5 seconds, S may be 400 Hz, and Nmay be 128 for an epilepsy monitoring unit such as depicted in FIG. 1.In an alternative exemplary embodiment, such as the real-time patientmonitoring and neurological event detection system 60 of FIG. 6utilizing implanted electrodes, N may be 16.

In an exemplary embodiment, the time windows k may be chosen tocollectively span the entire duration of the multi-channel signal, i.e.,the time windows are non-overlapping and contiguous in time. Inalternative exemplary embodiments, the time windows need not becontiguous in time, and may be spread out over the duration of themulti-channel signal. In this way, the time windows effectivelysub-sample the total duration of the multi-channel signal. Thissub-sampling may result in fewer matrices A[k] to be processed ascompared to using contiguous time windows, reducing the computationalcomplexity. In yet other alternative exemplary embodiments, the timewindows also may be made overlapping in time. In an exemplaryembodiment, the signals for the channels may be represented using areferential montage, wherein each signal is measured as an electricalpotential with respect to a common (“ground”) contact placed somewhereelse in the body; e.g., an “earlobe” in a scalp electroencephalogram.

At step 340, a condition number C[k] is computed for each matrix A[k] asfollows:

$\begin{matrix}{{{C\lbrack k\rbrack} = \frac{\max\left\{ \sigma_{i\; k} \right\}}{\min\left\{ \sigma_{i\; k} \right\}}};} & \left( {{Equation}\mspace{14mu} 1} \right)\end{matrix}$wherein {σ_(ik)} is the set of singular values of each matrix A[k]. Inan exemplary embodiment, the singular values of each matrix A[k] may becomputed using a singular-value decomposition (SVD) well-known in theart:A[k]=USV ^(T);  (Equation 2)wherein U and V are both square unitary matrices, and S contains thesingular values of A[k]. One of ordinary skill in the art willappreciate that a variety of software tools are available to calculatethe singular values, or approximate singular values, of a given matrix,for the purpose of deriving the matrix S. Such software tools, include,e.g., the publicly available software packages LAPACK or EISPACK.

At step 350, the condition number C[k] is further refined by conversioninto a “data condition number” DCN[k]. In an exemplary embodiment,DCN[k] may be computed as:

$\begin{matrix}{{{DCN}\lbrack k\rbrack} = {1 + {\frac{\left( {{C\lbrack k\rbrack} - 1} \right)}{N}.}}} & \left( {{Equation}\mspace{14mu} 3} \right)\end{matrix}$

The conversion from a condition number into a data condition number maybe done to compensate for an expected quasi-linear increase in thecondition number due to the number of channels N. Following theconversion, the data condition number may generally be processedindependently of the number of channels. One of ordinary skill in theart will appreciate that alternative techniques for accounting for thenumber of channels may be employed. For example, the condition numberneed not be converted to a data condition number, and the thresholdsused to determine the presence of anomalies may instead be adjusted bythe number of channels. Such alternative exemplary embodiments are alsocontemplated to be within the scope of the present disclosure.

One of ordinary skill in the art will further appreciate that, inalternative exemplary embodiments, either the condition number C[k] orthresholds used may be alternatively, or further, normalized withrespect to any source of variation in C[k] that is not of interest,i.e., not indicative of an anomaly. For example, variables such as thewindow size, signal measurement bandwidth, electrode montage, etc., mayalso be accounted for in converting the condition number C[k] to thedata condition number DCN[k], and/or choosing the thresholds againstwhich the DCN[k] is compared. Such additional coefficients andparameters may be calibrated empirically, and one of ordinary skill inthe art may readily modify Equation 3 accordingly to account for suchcoefficients and parameters.

According to the present disclosure, the magnitude of the data conditionnumber DCN[k] may serve as an indicator of whether a time window k of amulti-channel signal contains anomalies. For example, the DCN[k] mayrange from a value of 1, indicative of “healthy” data that is lacking inanomalies, to an arbitrarily large value ∞, indicative of “ill” datacorresponding, e.g., to complete flatlining over the time window ofinterest. Evaluating the magnitude of each DCN[k] may provide anindication of whether a signal anomaly is present in the correspondingtime window k.

In alternative exemplary embodiments (not shown), the DCN may becomputed for a subset of the total number of signal sensors byconstructing the matrix A[k] using such subset of signals, and employingthe number of signals in the subset for the variable N. For example,instead of employing all N channels to construct the matrix A[k], onlythe signals from a subset N−1 of the channels may be used. This may beadvantageous when, e.g., one of the signal sensors is known to befaulty. Such exemplary embodiments are contemplated to be within thescope of the present disclosure.

In alternative exemplary embodiments, the DCN may be computed from asubset N−1 of the channels to account for the effect of averagemontages, circular bipolar montages, or any other electrode montages,wherein one channel is known a priori to be a linear combination of allremaining channels, and therefore the matrix A[k] is rank-deficient.

One of ordinary skill in the art will appreciate that in light of thepresent disclosure, various alternative anomaly metrics to the conditionnumber for detecting the presence of anomalies may be derived, based on,e.g., determining the independence or correlation between two or more ofthe channels. These alternative metrics may be derived based on theassumption that healthy data, especially in neurological multi-channelsignals, is associated with independence among the channels, while datacontaining anomalies is associated with a lack of independence among thechannels.

For example, for a square matrix A[k], a generalized condition numberC′[k] may be derived as:C′[k]=Norm(A[k])·Norm(A ⁻¹ [k]);  (Equation 4)wherein Norm(·) denotes a norm of the matrix in parentheses, and A⁻[k]is the inverse of the square matrix A[k]. For example, norms such as theL1-norm and L-infinity norm are well-known in the art, and may beapplied to compute an anomaly metric associated with a matrix A[k] inthe following manner. One of ordinary skill in the art will appreciatethat the L1-norm may be defined as the maximum of the column sums ofA[k], and the L-infinity norm may be defined as the maximum of the rowsums.

As the computation of the generalized condition number C′ [k] and othertypes of condition numbers may require that the matrix A[k] be, e.g., asquare matrix, or have other pre-specified dimensions, the data from themulti-channel signals may be suitably modified to ensure that the matrixA[k] takes on the proper form. For example, to ensure that the matrixA[k] is a square matrix, the size of the time window may be chosen suchthat the number of discrete time samples for each channel is equal tothe total number of channels. Alternatively, the discrete time samplesover a given time window may be sub-sampled at regular intervals toarrive at a square matrix A[k]. Such exemplary embodiments arecontemplated to be within the scope of the present disclosure.

FIG. 4A illustrates a data condition number time series 400 obtainedfrom a sample multi-channel signal using the steps of FIG. 3A andthresholding for event detection. In FIG. 4A, the horizontal axisdepicts the time window index k, while the vertical axis depicts thevalue of DCN[k] corresponding to k. Note the absolute values of DCN[k]shown in FIG. 4A and discussed below are given for illustration only,and are not meant to restrict the techniques of the present disclosureto any particular values of DCN[k] shown. In particular, the absolutevalues of DCN[k] may depend on various parameters of the application.

In FIG. 4A, DCN[k] is shown to take on values generally less than 500prior to time window k=K1. Between k=K1 and k=K2, DCN[k] isapproximately 1000, while at time k=K2, DCN[k] is greater than 2000. Inan exemplary embodiment, one or more predetermined thresholds may bechosen to identify the presence of anomalies in the multi-channelsignal. For example, in FIG. 4A, the DCN[k] may be compared to a singlethreshold T1, and values of DCN[k] that are greater than the thresholdT1 may indicate the presence of an “event” in the corresponding timewindow k. In an exemplary embodiment, the presence of an event may betaken to indicate the presence of an anomaly.

Further shown in FIG. 4A is an anomaly plot 410 generated from the timeseries 400 by comparison with threshold T1. Note anomaly plot 410identifies the presence of two events A and B, possibly corresponding totwo anomalies.

In alternative exemplary embodiments, two or more thresholds may bechosen for more precise categorization of the DCN. For example, in anexemplary embodiment, two thresholds T1 and T2 may be chosen, whereinT1<T2. In this exemplary embodiment, if the DCN is less than T1, then alack of anomaly in the time window may be declared. If the DCN isgreater than T2, then an anomaly can be automatically declared. If theDCN is between T1 and T2, then further processing, such as manualinspection of the multi-channel signal, may be performed to determinewhether an anomaly is actually present. In an exemplary embodiment,patient-specific thresholds may be chosen that are customized to anindividual patient whose neurological or other biological state is beingmonitored by the multiple sensors. Thresholds may be set differently fordifferent patients, to account for the unique characteristics of eachpatient's bio-signals.

According to an aspect of the present disclosure, specific thresholdsmay be automatically chosen for any patient or group of patients using aprocedure as described with reference to the plot 400A shown in FIG. 4B.In FIG. 4B, the horizontal axis shows a candidate threshold T1, whilethe vertical axis tallies a number of events detected for thecorresponding candidate threshold. Based on the plot 400A, an optimumthreshold T1* may be chosen to, e.g., maximize the probability ofdetecting an anomalous event while minimizing the probability of “false”positives. In an exemplary embodiment, as shown in FIG. 4B, such anoptimum threshold T1* may be chosen as, e.g., an inflection point of theplot 400A having the largest abscissa from amongst all inflectionpoints, wherein an inflection is defined as a point where the curvaturechanges sign, e.g., from facing down to facing up. Alternatively, ifsuch an inflection point does not exist (e.g., in exponential decay),the threshold T1* may be chosen as the minimum abscissa where the plot400A drops to between 1% and 2% of its maximum value Nmax.

FIGS. 4C and 4D illustrate an alternative exemplary embodiment of atechnique for choosing an optimum threshold T1*. FIG. 4C illustrates anexemplary histogram of DCN (horizontal axis), with the vertical axisshowing the number of instances of the corresponding DCN in an arbitrarymulti-channel signal (not shown). From the histogram of FIG. 4C, one ofordinary skill in the art may readily derive a corresponding cumulativedistribution function (CDF), as shown in FIG. 4D, which indicates whatpercentage of events correspond to DCN's equal to or less than the shownDCN. One of ordinary skill in the art will appreciate that by selectingan appropriate percentage (e.g., 90% as illustrated in FIG. 4D), acorresponding threshold DCN of T1*, such as T1*_(90%) in FIG. 4D, may beautomatically determined using the functional mapping provided by theCDF. One of ordinary skill in the art will further appreciate that in anexemplary embodiment, a computer may be programmed to perform suchthreshold selection, such that the choice of threshold may be made fullyautomatic without the need for manual intervention.

While certain exemplary techniques for automatically a choosing asuitable threshold T1* have been disclosed hereinabove, one of ordinaryskill in the art will appreciate that alternative techniques notexplicitly described may be readily derived in light of the presentdisclosure. Such alternative exemplary embodiments are contemplated tobe within the scope of the present disclosure.

In alternative exemplary embodiments, additional properties of the DCNmay be analyzed to further aid in the detection of anomalies in themulti-channel signal. For example, the rate of change of the DCN over apredetermined interval of time may also be utilized to detect thepresence of an anomaly. Such modifications to the DCN and others notexplicitly described will be clear to one of ordinary skill in the art,and are contemplated to be within the scope of the present disclosure.

FIG. 5 depicts an exemplary method according to the present disclosurefor taking action in response to the detection of an anomaly.

At step 510, a DCN time series such as 400 in FIG. 4A is generated froma multi-channel signal. In an exemplary embodiment, such a time seriesmay be generated according to the method 300 depicted in FIG. 3A.However, one of ordinary skill in the art will appreciate thatalternative methods may be employed to generate a suitable time seriesin light of the present disclosure.

At step 520, an anomaly log is generated based on the DCN time series.Such an anomaly log may identify, e.g., time window indices k in the DCNtime series corresponding to detected anomalies. For example, in theexemplary embodiment wherein DCN[k] is compared to a single threshold T1to determine the presence of an anomaly, the anomaly log may record alltime window indices k wherein DCN[k] is larger than T1. As such, theanomaly log may effectively capture the relevant information from theanomaly plot 410.

One of ordinary skill in the art will appreciate that the information inan anomaly log may be recorded in several ways. For example, each linein the anomaly log may record the time index k associated with thebeginning of a detected anomaly, and the corresponding time duration ofthe detected anomaly. Alternatively, the start and stop time indicesassociated with each detected anomaly may be recorded. Such exemplaryembodiments are contemplated to be within the scope of the presentdisclosure.

At step 530, the complexity of the anomaly log may be reduced by mergingseparate anomalies that are separated by less than a minimum timeseparation. For example, assume two anomalies each of duration 10 arefound in an anomaly log starting at time windows k=1 and k=12, i.e., thetwo anomalies are separated by a time duration of Δk=1. If a minimumtime separation is defined as Δk_(min)=5, then the two anomalies may bemerged to form a single anomaly, which can be recorded in a simplifiedanomaly log as a single merged anomaly of duration Δk=21 starting atk=1.

By performing the merging as described at step 530, the number ofrecorded anomalies and the size of the resulting anomaly log may bereduced to facilitate subsequent processing.

At step 540, the segments of the original multi-channel signalcorresponding to the detected anomalies are identified.

At step 550, the identified segments of the multi-channel signal may bestored in an output record for post-processing. For example, the outputrecord may be a computer file stored in a storage medium such as acomputer hard drive, or it may be a paper print-out. In an exemplaryembodiment, the identified segments output to the file may be expandedbeyond those strictly associated with the anomalies. For example, fixedtime segments of the multi-channel signal both immediately prior to andimmediately subsequent to each identified data anomaly may also beoutput for each identified segment corresponding to an anomaly. Theadditional segments may further aid in the post-processing of theanomalies in the multi-channel signal, as further described hereinbelow.

In an exemplary embodiment (not shown), the output record generated bythe method 500 may be manually reviewed, or “scrubbed,” by a humantechnician to verify the presence of anomalies in the identifiedmulti-channel signal segments. If the identified segment is verified tocontain an anomaly, the segment may be, e.g., omitted from furtherpost-processing, or other measures may be taken.

FIG. 6 depicts an alternative exemplary embodiment of the presentdisclosure, wherein the techniques disclosed hereinabove are applied inthe context of a real-time patient monitoring and neurological eventdetection system 60. For a more detailed description of the system inFIG. 6, see, e.g., “Minimally Invasive Monitoring Methods,” U.S. patentapplication Ser. No. 11/766,751, filed Jun. 21, 2007, published as U.S.Patent Publication No. 2008/0027347A1 and assigned to the assignee ofthe present application, the contents of which are hereby incorporatedby reference in their entirety. Note that FIG. 6 is provided forillustrative purposes only, and is not meant to limit the scope of thepresent disclosure in any way.

In FIG. 6, system 60 includes one or more implantable sensors or devices62 that are configured to sample electrical activity from the patient'sbrain (e.g., EEG signals). The implantable devices may be active (withinternal power source), passive (no internal power source), orsemi-passive (internal power source to power components, but not totransmit data signal). The implantable devices 62 may be implantedanywhere in the patient. In an exemplary embodiment, one or more of thedevices 62 may be implanted adjacent to a previously identifiedepileptic focus or a portion of the brain where the focus is believed tobe located. Alternatively, the devices 62 themselves may be used to helpdetermine the location of an epileptic focus

In one aspect, the neural signals of the patient are sampledsubstantially continuously with the electrodes coupled to the electroniccomponents of the implanted leadless device. A wireless signal istransmitted that is encoded with data that is indicative of the sampledneural signal from the implanted device to an external device. Thewireless signal that is encoded with data that is indicative of thesampled neural signal is derived from the wireless signal received fromthe external device. The wireless signal can be any type of wirelesssignal—radiofrequency signal, magnetic signal, optical signal, acousticsignal, infrared signal, or the like.

The physician may implant any desired number of devices in the patient.As noted above, in addition to monitoring brain signals, one or moreadditional implanted devices 62 may be implanted to measure otherphysiological signals from the patient.

Implantable devices 62 may be configured to substantially continuouslysample the brain activity of the groups of neurons in the immediatevicinity of the implanted device. The implantable devices 62 may beinterrogated and powered by a signal from an external device 64 tofacilitate the substantially continuous sampling of the brain activitysignals. Sampling of the brain activity may be carried out at a rateabove about 200 Hz, and preferably between about 200 Hz and about 1000Hz, and most preferably at about 400 Hz, but it could be higher orlower, depending on the specific condition being monitored, the patient,and other factors. Each sample of the patient's brain activity maycontain between about 8 bits per sample and about 32 bits per sample,and preferably between about 12 bits per sample and about 16 bits persample.

In alternative embodiments, it may be desirable to have the implantabledevices sample the brain activity of the patient on a non-continuousbasis. In such embodiments, the implantable devices 62 may be configuredto sample the brain activity signals periodically (e.g., once every 10seconds) or aperiodically.

Implantable devices 16 may comprise a separate memory module for storingthe recorded brain activity signals, a unique identification code forthe device, algorithms, other programming, or the like.

A patient instrumented with the implanted devices 62 may carry a datacollection device 64 that is external to the patient's body. Theexternal device 64 would receive and store the signals from theimplanted devices 62 with the encoded EEG data (or other physiologicalsignals). The signals received from the plurality of implanted devices62 may be represented as a multi-channel signal, and may bepre-processed according to the techniques of the present disclosure. Theexternal device 64 is typically of a size so as to be portable andcarried by the patient in a pocket or bag that is maintained in closeproximity to the patient. In alternative embodiments, the device may beconfigured to be used in a hospital setting and placed alongside apatient's bed. Communication between the data collection device 64 andthe implantable device 62 may take place through wireless communication.The wireless communication link between implantable device 62 andexternal device 64 may provide a communication link for transmittingdata and/or power. External device 64 may include a control module 66that communicates with the implanted device through an antenna 68. Inthe illustrated embodiment, antenna 68 is in the form of a necklace thatis in communication range with the implantable devices 62.

Transmission of data and power between implantable device 62 andexternal device 64 may be carried out through a radiofrequency link,infrared link, magnetic induction, electromagnetic link, Bluetooth®link, Zigbee link, sonic link, optical link, other types of wirelesslinks, or combinations thereof.

In an exemplary embodiment, the external device 64 may include softwareto pre-process the data according to the present disclosure and analyzethe data in substantially real-time. For example, the received RF signalwith the sampled EEG may be analyzed for the presence of anomaliesaccording to the present disclosure, and further by EEG analysisalgorithms to estimate the patient's brain state which is typicallyindicative of the patient's propensity for a neurological event. Theneurological event may be a seizure, migraine headache, episode ofdepression, tremor, or the like. The estimation of the patient's brainstate may cause generation of an output. The output may be in the formof a control signal to activate a therapeutic device (e.g., implanted inthe patient, such as a vagus nerve stimulator, deep brain or corticalstimulator, implanted drug pump, etc.).

In an exemplary embodiment, the output may be used to activate a userinterface on the external device to produce an output communication tothe patient. For example, the external device may be used to provide asubstantially continuous output or periodic output communication to thepatient that indicates their brain state and/or propensity for theneurological event. Such a communication could allow the patient tomanually initiate self-therapy (e.g., wave wand over implanted vagusnerve stimulator, cortical, or deep brain stimulator, take a fast actinganti-epileptic drug, etc.).

In an alternative exemplary embodiment, the external device 64 mayfurther communicate with an auxiliary server (not shown) having moreextensive computational and storage resources than can be supported inthe form factor of the external device 64. In such an exemplaryembodiment, the anomaly pre-processing and EEG analysis algorithms maybe performed by an auxiliary server, or the computations of the externaldevice 64 may be otherwise facilitated by the computational resources ofthe auxiliary server.

FIG. 7 depicts a generalized block diagram 700 of the real-time analysissystem 60 featuring an anomaly pre-processing block 735 according to thepresent disclosure. One of ordinary skill in the art will appreciatethat the block diagram 700 need not be limited to the exemplary system60 shown in FIG. 6, but may also be broadly applicable to other types ofmulti-channel sensing and processing systems.

In FIG. 7, biosensor signals 710 form a multi-channel signal that isprovided over a wireless link to wireless unit 720. Wireless unit 720communicates the multi-channel signal to a processing module 730 thatmay be resident either on the wireless unit 720 itself, or separatelyfrom the wireless unit 720, as described with reference to FIG. 6. Whenresiding separately from the wireless unit 720, the processing module730 may be configured to run algorithms, perform computations, orperform anomaly checking that may be too complex or intensive for alow-power wireless unit 720 to implement. Such anomaly checking maycorrespond to the “self-checking” techniques as further describedhereinbelow with reference to FIG. 8.

Processing module 730 includes a pre-processing block 735 thatidentifies and processes anomalies in the multi-channel signal. Theoutput of pre-processing block 735 is provided to a data analysis block737, which may output an event indicator 730 a. The output eventindicator 730 a may correspond to the output of the estimation of thepatient's brain state as described with reference to FIG. 6.

In the exemplary embodiment shown, the pre-processing block 735communicates with an anomaly data service 740. The anomaly data service740 may reside remotely from the processing module 730, and may providethe pre-processing block 735 with dynamically adjusted thresholds and/orother parameters to aid the pre-processing block 735 in identifyinganomalies in the multi-channel signal. For example, the anomaly dataservice 740 may analyze anomalies from a plurality of multi-channelsignals sampled over a population of seizure detection systems, seizureprediction systems, and/or seizure counter-prediction systems. Theanomaly data service 740 may periodically derive preferred DCNcomparison thresholds for use in the individual real-time analysissystem 700. In an exemplary embodiment, the real-time analysis system700 may also upload data samples to the anomaly data service 740 to aidthe anomaly data service 740 in deriving preferred thresholds.

In an exemplary embodiment, the anomaly data service 740 may communicatewith the processing module 730 wirelessly. Alternatively, the anomalydata service 740 may communicate with the processing module 730 over awired connection. In yet another exemplary embodiment, the anomaly dataservice 740 may be omitted altogether, and the pre-processing block 735may simply rely on pre-programmed threshold values. Such exemplaryembodiments are contemplated to be within the scope of the presentdisclosure.

FIG. 8 depicts an exemplary method 800 according to the presentdisclosure that may be implemented, e.g., using the system 700. Note theexemplary method is shown for illustrative purposes only, and is notmeant to limit the scope of the present disclosure to any particularmethod disclosed.

At step 810, the wireless unit 720 and processing module 730 are poweredon.

At step 820, the wireless unit 720 receives the multi-channel signalfrom, e.g., a plurality of biosensors such as implanted devices 62 inFIG. 6.

At step 830, anomaly pre-processor 735 in processing module 730identifies the presence of boot-up anomalies in the multi-channel signalreceived at step 820. This step may also be termed “self-checking,” or“self-test diagnostics.”

In an exemplary embodiment, “boot-up” anomalies may be any anomaliesidentified in the multi-channel signal during an initial boot-up phase.The boot-up phase may correspond to a time when software in theprocessing module 730 is initialized, and/or other parameters of thesystem 700 are initially configured. For example, the boot-up phase maylast for a fixed amount of time after the wireless unit 720 andprocessing module 730 are powered on at step 800.

In an exemplary embodiment, the identification of anomalies in themulti-channel signal may be performed using the DCN computationtechniques earlier described herein with reference to FIG. 3A. However,anomaly identification need not be performed using DCN computation.Anomaly identification in the method 800 may generally be performedusing any suitable anomaly detection metric or metrics derivable by oneof ordinary skill in the art in light of the present disclosure. Suchalternative exemplary embodiments are contemplated to be within thescope of the present disclosure.

At step 840, an anomaly central data service may be continuously updatedduring operation of the method 800 with appropriate thresholds and/oralgorithms for detecting the presence of anomalies in the multi-channelsignal. In an exemplary embodiment, the anomaly data service may updatea series of thresholds T1, T2, etc., against which the data conditionnumber (DCN) is compared to detect the presence of anomalies in themulti-channel signal. The anomaly data service may vary the value ofsuch thresholds over time, based on, e.g., offline analysis of anomaliesand associated anomaly metrics as computed over an entire population ofmulti-channel signals.

At step 850, operation of the system 700 proceeds with the processingmodule 730 processing the multi-channel signal, taking into account theinformation in the anomaly data service.

At step 860, the anomaly processor 735 checks for anomalies in themulti-channel signal during normal operation of the system 700. Thechecking at step 860 may be termed “adaptive” anomaly identification andprocessing, as contrasted with the “boot-up” anomaly identification andprocessing described with reference to step 830. Information aboutanomalies identified during step 860 may be used to update the anomalydata service, as illustrated by the return arrow from step 860 to step840, and as earlier described with reference to block 740 hereinabove.The steps 840, 850, 860 may be continuously repeated during normaloperation of the system 700. An advantage of the adaptive anomalyidentification and processing techniques described herein is that theymay be varied over an extended temporal context used to monitor themulti-channel signal, as compared to the one-time self-checkingdiagnostics provided during a boot-up phase.

In an exemplary embodiment, entries from the anomaly data service mayalso be removed from the data service if anomaly processor 735determines that such anomalies are no longer applicable. Such exemplaryembodiments are contemplated to be within the scope of the presentdisclosure.

FIG. 9A illustrates an exemplary plot of data condition number (DCN)versus time, corresponding to an operating scenario wherein two of thesensors used to generate a multi-channel signal are graduallyelectrically shorted together during the interval starting from Time=5minutes to Time=10 minutes. The scenario shown may arise, e.g., when twoadjacent connectors at a distal end of a lead assembly implanted in apatient are gradually shorted together, effectively forming a path forinterconnect ionic migration.

As seen in FIG. 9A, the DCN increases to extremely high levels inresponse to the gradual electrical shorting, and takes on values as highas 10¹⁵ when two of the leads are eventually completely shortedtogether, and converging to their common average, after Time=10 minutes.This expected behavior of the DCN in response to a physical conditionsuch as electrical shorting allows the DCN to be used as an effectivedetection metric for indicating when such conditions might arise in anactual scenario. For example, by detecting when the DCN exceeds asuitably high threshold, the error condition wherein there is electrodeshortage may be flagged to an automated software module, or to a manualuser. One of ordinary skill in the art will appreciate that alternativetechniques may also be employed to detect such an error condition, e.g.,pattern matching the observed DCN to expected behavior of the DCN in thepresence of the error condition, such expected behavior being obtainedthrough, e.g., prior simulation or historical data. Furthermore, throughfurther processing, e.g., by computing and evaluating the DCN overspecific subsets of channels of the multi-channel signal, the specificleads causing the electrical shortage problem may be specificallyidentified and dealt with.

FIG. 9B illustrates an exemplary plot of data condition number (DCN)versus time, corresponding to an operating scenario wherein there is agradual loss of contact of a reference electrode used to sample themulti-channel signal, over the span of about three days. As seen in FIG.9B, there is a noticeable upward trend in the DCN over the time intervalshown. In an exemplary embodiment, the presence of such an upward trendin the DCN may be used to detect the condition corresponding to gradualloss of contact of an electrode. Such a condition may then be flagged toan automated software module, or to a manual user. One of ordinary skillin the art will appreciate that various techniques may be employed toquantify any upward trend in the DCN. For example, a low-pass filter maybe applied to the DCN time-series, and absolute increases in the DCNover a suitably long time period may be assessed and compared to somethreshold. Alternatively, other pattern matching techniques may also beemployed. Such alternative exemplary embodiments are contemplated to bewithin the scope of the present disclosure.

FIG. 10A illustrates an exemplary embodiment of a graphical displayinterface 1000 for simultaneously displaying various types ofinformation associated with the techniques of the present disclosure.Note the exemplary embodiment of FIG. 10A is shown for illustrativepurposes only, and is not meant to limit the scope of the presentdisclosure to any particular graphical display interface shown. One ofordinary skill in the art will appreciate that the graphical displayinterface 1000 may be a “screen shot,” i.e., an instantaneous sample ofthe image on a computer screen displayed when a user (e.g., atechnician) is using the disclosed graphical interface to analyze data.The graphical display may be generated by, e.g., computer softwarerunning on a general-purpose computer, and may be readily designed byone of ordinary skill in the art in light of the further descriptiongiven hereinbelow. For example, in an exemplary embodiment, suchcomputer software may be conveniently accessed via a Web browser.

In FIG. 10A, graphical display interface 1000 includes a graphicalrepresentation of a catalog configuration block 1010, a graphicalrepresentation of a legend 1020, and a series of time-series displays1040. The time-series displays 1040 may include time series 1041, 1042,1043, each associated with a corresponding subject identification numberas denoted in the subject ID column 1030. In the exemplary embodimentshown, there are three subject ID's 0001, 0002, and 0003. One ofordinary skill in the art will appreciate that the techniques disclosedherein may readily be applied to display information associated with anynumber of subjects, per the requirements of the application.

The catalog configuration block 1010 includes a series of drop-downmenus 1011, 1012, and 1013.

Drop-down menu 1012 allows the user to select a scheme for backdroppatterns used to highlight certain information contained in thetime-series displays 1040. In an exemplary embodiment, the mappingbetween a type of information and a corresponding backdrop pattern usedto highlight such information type may be as shown according to thelegend 1020. The legend 1020 indicates backdrop patterns used to show an‘interictal’ segment 1021, a ‘preseizure’ segment 1022 (e.g., a fixedpreictal time interval), other segment 1023, dropout 1024 (e.g., lapsedor invalid data), and seizure event 1025. As shown in the time-series1041, 1042, and 1043, the backdrop patterns identified in legend 1020may be used to annotate the time-series displays 1040 with informationin a concise and easily presented form for convenient analysis by a userof the graphical display interface 1000.

One of ordinary skill in the art will appreciate that variousmodifications to the backdrop pattern scheme shown are readily derivablein light of the present disclosure. For example, in a color graphicaldisplay interface (not shown), backdrop colors may be used in place of,or in addition to, the backdrop patterns shown. A color scheme mayassign a distinct color to each of the following types of segments:“interictal” segments, “preseizure” segments, “other” segments,“dropouts” (e.g., lapsed or invalid data), and “seizure” events.Alternative exemplary embodiments may also include other types ofinformation not explicitly shown in FIG. 10A. Such alternative exemplaryembodiments are contemplated to be within the scope of the presentdisclosure.

Drop-down menu 1013 allows the user to select the quantity to bedisplayed on the vertical axes of the time-series displays 1040. Forexample, one selectable quantity may be a data condition number (DCN) ascalculated for each subject according to the present disclosure, plottedversus time. Alternative types of quantities include, e.g., thechannel-sum of line-lengths (sum of absolute deviations in time) or itsspatiotemporal and normalized variants, which would be appropriate forlarge-scale temporal localization of seizures rather than EEG anomalies.

As further shown in FIG. 10A, time series 1041 is accompanied by a timeevent index 1041.1. In the exemplary embodiment shown, the time eventindex 1041.1 is shown in bold and underlined, and has a numerical valueof 20108. The time event index 1041.1 may indicate a time index in thetime series 1041 wherein the corresponding DCN value exceeds somepre-specified threshold. See, e.g., the portion of time series 1041indicated by 1041.1 a. As more clearly shown in FIG. 10B, by rolling acursor 1080A over the time event index 1041.1, an auxiliary window 1090Amay “pop up” in the same display. Alternatively, the time event index1041.1 may be “hyperlinked,” such that placing the cursor 1080A over thetime event index 1041.1 and clicking the index 1041.1 causes theauxiliary window 1090A to simultaneously pop up.

The information content, or type of event, shown in the auxiliary window1090A may be selected via the drop-down menu 1011. In the exemplaryembodiment shown in FIG. 10B, the auxiliary window 1090A may show, e.g.,the portion of the original multi-channel signal (such as EEG data) fromwhich the DCN value corresponding to the time event index 1041.1 wasderived. The auxiliary window 1090A advantageously allows the graphicalinterface user to visually inspect the portion of the multi-channelsignal giving rise to the above-threshold DCN value alongside the DCNtime-series, and manually determine whether the identified portion is infact anomalous.

Other pop-up events to display may include context menus that direct theuser to the raw EEG data where an anomaly can be further inspected infull detail.

As shown for indices 1043.1 and 1043.2 associated with time-series 1043,there may be multiple points in a time series wherein a DCN valueexceeds a pre-specified threshold, and thus there may generally bemultiple event indices associated with each time-series.

Based on the teachings described herein, it should be apparent that anaspect disclosed herein may be implemented independently of any otheraspects and that two or more of these aspects may be combined in variousways. In one or more exemplary embodiments, the functions described maybe implemented in hardware, software, firmware, or any combinationthereof. If implemented in software, the functions may be stored on ortransmitted over as one or more instructions or code on acomputer-readable medium. Computer-readable media includes both computerstorage media and communication media including any medium thatfacilitates transfer of a computer program from one place to another. Astorage media may be any available media that can be accessed by acomputer. By way of example, and not limitation, such computer-readablemedia can comprise RAM, ROM, EEPROM, CD/DVD-ROM or other optical diskstorage, magnetic disk storage or other magnetic storage devices,solid-state flash cards or drives, or any other medium that can be usedto carry or store desired program code in the form of instructions ordata structures and that can be accessed by a computer. Also, anyconnection is properly termed a computer-readable medium. For example,if the software is transmitted from a website, server, or other remotesource using a coaxial cable, fiber optic cable, twisted pair, digitalsubscriber line (DSL), or wireless technologies such as infrared, radio,and microwave, then the coaxial cable, fiber optic cable, twisted pair,DSL, or wireless technologies such as infrared, radio, and microwave areincluded in the definition of medium. Disk and disc, as used herein,includes compact disc (CD), laser disc, optical disc, digital versatiledisc (DVD), floppy disk and Blu-Ray disc where disks usually reproducedata magnetically, while discs reproduce data optically with lasers.Combinations of the above should also be included within the scope ofcomputer-readable media.

In this specification and in the claims, it will be understood that whenan element is referred to as being “connected to” or “coupled to”another element, it can be directly connected or coupled to the otherelement or intervening elements may be present. In contrast, when anelement is referred to as being “directly connected to” or “directlycoupled to” another element, there are no intervening elements present.

A number of aspects and examples have been described. However, variousmodifications to these examples are possible, and the principlespresented herein may be applied to other aspects as well. These andother aspects are within the scope of the following claims.

What is claimed is:
 1. A method for displaying information to a user,the method comprising: accepting input from the user selecting a metricto be displayed, said selected metric being associated with amulti-channel signal derived from outputs of a plurality of sensorspositioned to acquire physiological signals, wherein each channel of themulti-channel signal corresponds to a different one of the plurality ofsensors; displaying with a processor and a computer graphical displayinterface at least one time-series plot of the selected metricassociated with the multi-channel signal; using a backdrop pattern,indicating on the at least one time-series plot portions of the plothaving at least one identified characteristic; for each of the at leastone time-series plot displayed, displaying with the processor and thecomputer graphical display interface a time event index wherein thecorresponding metric meets a predetermined condition, wherein thepredetermined condition comprises the selected metric being greater thana predetermined threshold; accepting input from the user as to whetherto display further information associated with the time event index; anddisplaying the further information associated with the time event indexwhen the user so specifies.
 2. The method of claim 1, the metriccomprising a data condition number calculated for the multi-channelsignal.
 3. The method of claim 1, the at least one time-series plotcomprising a plurality of time-series plots, each time-series plotassociated with a subject ID.
 4. The method of claim 1, the at least oneidentified characteristic including a portion of the time-seriescorresponding to a seizure segment.
 5. The method of claim 1, thebackdrop pattern comprising a color scheme, the color schemerepresenting “interictal” segments, “preseizure” segments, “other”segments, “dropouts,” and “seizure” events.
 6. The method of claim 1,the input from the user as to whether to display further informationassociated with the time event index comprising positioning of a cursorover the time event index.
 7. The method of claim 1, further comprising:accepting input from the user indicating a color scheme for the backdroppattern to be used.
 8. The method of claim 1, the displaying the furtherinformation associated with the time event index comprising: displayinga pop-up plot of a portion of the multi-channel signal corresponding tothe time event index.
 9. The method of claim 8, the multi-channel signalcomprising a multi-channel EEG signal.
 10. A method for detectinganomalies in a multi-channel signal, the method comprising: sampling themulti-channel signal over a time window; computing with a processor ananomaly metric for the multi-channel signal over the time window; andidentifying the presence of an anomaly based on the magnitude of theanomaly metric; the computing an anomaly metric comprising: computing acondition number of the multi-channel signal over the time window; andadjusting the condition number based on a parameter of the multi-channelsignal to generate a data condition number (DCN); the multi-channelsignal comprising a signal sampled from a plurality of electrodespositioned to acquire physiological signals from a body, the methodfurther comprising: generating a DCN time series corresponding to aplurality of time windows; generating an anomaly log based on the DCNtime series; merging anomalies in the anomaly log separated by less thana minimum separation to generate a modified anomaly log; identifyingsegments of the multi-channel signal corresponding to anomalies in themodified anomaly log; and outputting time-expanded versions of theidentified segments to a record; the identifying the presence of ananomaly comprising matching the DCN time series to at least one knownpattern of DCN time series corresponding to an anomalous condition. 11.A method for detecting anomalies in a multi-channel signal, the methodcomprising: sampling the multi-channel signal over a time window;computing with a processor an anomaly metric for the multi-channelsignal over the time window; and identifying the presence of an anomalyby comparing the magnitude of the anomaly metric to an optimumthreshold; the computing an anomaly metric comprising: computing acondition number of the multi-channel signal over the time window; andadjusting the condition number based on a parameter of the multi-channelsignal to generate a data condition number (DCN); the method furthercomprising: generating a DCN time series corresponding to a plurality oftime windows; generating an event log based on the DCN time series, thegenerating an event log comprising identifying an event as a contiguousset of DCN values greater than a candidate threshold; merging events inthe event log separated by less than a minimum separation to generate amodified event log; repeating the steps of generating a DCN time series,generating an event log, and merging events using a plurality ofcandidate thresholds; generating a plot of number of events identifiedin a modified event log versus candidate threshold used; attempting toidentify at least one inflection point in the generated plot; andsetting the optimum threshold to be the candidate thresholdcorresponding to the inflection point with the largest abscissa in theplot.
 12. The method of claim 11, further comprising, when the attemptto identify at least one inflection point is unsuccessful: identifying amaximum number of events corresponding to a candidate threshold in thehistogram; and setting the optimum threshold to be a candidate thresholdwhose corresponding number of events is less than or equal to a fixedpercentage of the maximum number of events.